| | --- |
| | license: mit |
| | base_model: microsoft/Florence-2-large-ft |
| | tags: |
| | - image-to-text |
| | - generated_from_trainer |
| | model-index: |
| | - name: Florence-2-large-FormClassification-ft |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # Florence-2-large-FormClassification-ft |
| |
|
| | This model is a fine-tuned version of [microsoft/Florence-2-large-ft](https://huggingface.co/microsoft/Florence-2-large-ft) on an Musa07/Florence_ft dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2107 |
| | |
| | ### Inference Code versions |
| | |
| | # Code |
| | from transformers import AutoProcessor, AutoModelForCausalLM |
| | import matplotlib.pyplot as plt |
| | import matplotlib.patches as patches |
| | |
| | model = AutoModelForCausalLM.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True, device_map='cuda') # Load the model on GPU if available |
| | processor = AutoProcessor.from_pretrained("Musa07/Florence-2-large-FormClassification-ft", trust_remote_code=True) |
| | |
| | def run_example(task_prompt, image, max_new_tokens=128): |
| | |
| | prompt = task_prompt |
| | inputs = processor(text=prompt, images=image, return_tensors="pt") |
| | generated_ids = model.generate( |
| | input_ids=inputs["input_ids"].cuda(), |
| | pixel_values=inputs["pixel_values"].cuda(), |
| | max_new_tokens=max_new_tokens, |
| | early_stopping=False, |
| | do_sample=False, |
| | num_beams=3, |
| | ) |
| | generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
| | parsed_answer = processor.post_process_generation( |
| | generated_text, |
| | task=task_prompt, |
| | image_size=(image.width, image.height) |
| | ) |
| | return parsed_answer |
| | |
| | def plot_bbox(image, data): |
| | |
| | fig, ax = plt.subplots() |
| | |
| | # Display the image |
| | ax.imshow(image) |
| | |
| | # Plot each bounding box |
| | for bbox, label in zip(data['bboxes'], data['labels']): |
| | # Unpack the bounding box coordinates |
| | x1, y1, x2, y2 = bbox |
| | # Create a Rectangle patch |
| | rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none') |
| | # Add the rectangle to the Axes |
| | ax.add_patch(rect) |
| | # Annotate the label |
| | plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5)) |
| | |
| | # Remove the axis ticks and labels |
| | ax.axis('off') |
| | |
| | # Show the plot |
| | plt.show() |
| | |
| | image = Image.open('1.jpeg') |
| | parsed_answer = run_example("<OD>", image=image) |
| | print(parsed_answer) |
| | plot_bbox(image, parsed_answer["<OD>"]) |
| | |
| |
|
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 1e-06 |
| | - train_batch_size: 24 |
| | - eval_batch_size: 24 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | 0.0188 | 1.0 | 23 | 0.2151 | |
| | | 0.0127 | 2.0 | 46 | 0.2113 | |
| | | 0.0078 | 3.0 | 69 | 0.2061 | |
| | | 0.0047 | 4.0 | 92 | 0.2102 | |
| | | 0.0042 | 5.0 | 115 | 0.2078 | |
| | | 0.003 | 6.0 | 138 | 0.2108 | |
| | | 0.0022 | 7.0 | 161 | 0.2110 | |
| | | 0.0029 | 8.0 | 184 | 0.2117 | |
| | | 0.0019 | 9.0 | 207 | 0.2114 | |
| | | 0.0023 | 10.0 | 230 | 0.2107 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.44.0.dev0 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
| |
|
